Purpose: This study explores how the relationship between supervisor's proactivity, job demands and job outcomes is based on dyadic interpersonal interaction based on the literature of the job demands-resources model and conservation of resources theory. Design/methodology/approach: In this work, valid data from 272 participants (241 information technology subordinates and 31 project managers) in professional service firms are used in measurement and structural analyses based on a cross-level research framework. Additionally, the hierarchical linear modeling technique and a cross-sectional dataset were used to evaluate the proposed hypotheses. Findings: The results reveal that supervisor proactivity is a critical resource during the execution of professional service projects and is significantly related to perceptions of job demands on the part of subordinates while positively moderating the relationship between job demands and job satisfaction and job demands organizational commitment. Originality/value: The answer to the question as to whether extensive use of job resources (i.e. supervisor proactivity) in service projects is beneficial and inconclusive in the current information technology (IT) industry literature. Currently, the IT industry continues to experience rapid growth and is a dynamic sector in the global economy that results in increased demands on supervisors because of the specific characteristics of their positions. Consequently, it is necessary further to examine both the direct and moderating effects of resource crossover driven by supervisor proactivity on subordinate behavior, including job demands, job satisfaction and organizational commitment. Although proactivity is a relatively mature concept, some issues related to the negative effects of proactivity on factors, such as job demands, technostress and addiction, need to be further addressed. However, studies specifically focus on investigating this issue are missing from the literature. The findings of this paper thus address these research gaps by validating the direct and moderating relationships of such factors using the proposed cross-level research model.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Library and Information Sciences